In this paper, we propose to use an enhanced version of the standard TDNN model based on Emphasized Channel Attention, Propagation, and Aggregation (ECAPA-TDNN) [1]}.
The ECAPA-TDNN model employs a channel- and context-dependent attention mechanism, Multilayer Feature Aggregation (MFA), as well as Squeeze-Excitation (SE) and residual blocks.
This model has recently shown impressive performance in the speaker verification domain [1]}.
It has shown the best performance in the text-independent task of SdSV Challenge on short-duration speaker verification [3]}, [4]}.
This makes it a good choice for speaker diarization, as speaker turns in realistic conversations can be of short duration.
This is the first time that the ECAPA-TDNN model is used in the context of speaker diarization.
Our work further improves its robustness by training the ECAPA-TDNN model with an extensive on-the-fly augmentation scheme that relies on the combination of different techniques for speech contamination.
We propose to use augmentation techniques such as waveform dropout, frequency dropout, speed perturbation, reverberation, and additive noise while training the ECAPA-TDNN model.
Differently from standard augmentation schemes, all the contaminated versions are concatenated within the same training batch.
